1. Field of the Invention
The present invention is concerned with facilitating comparison of a medical image to a database relating to content of the medical image.
2. Description of the Prior Art
Comparing medical images of a patient to a database of scans acquired from normal subjects is a well-known technique for trying to highlight abnormalities in the patient scan: Equivalently, the patient could be compared to a database of scans showing a certain pattern of disease to look for either similarities or differences that can be used in the diagnostic process; these abnormalities can then be used in the process of diagnosis.
The process of comparison generally takes the form of the following steps:                The patient's scan must be spatially normalized, that is to say that it must be possible to form a spatial correspondence between the patient's scan and the database to which it is being compared        The patient's scan must be intensity normalized, that is to say that the intensity of voxels in the patient's scan must be normalised to remove, for example, differing rates of uptake in the tracer due to each patient's unique metabolism.        
Additionally, a degree of smoothing (e.g., Gaussian) may be applied to the data to reduce noise and reduce some of the differences in image appearance that arise from different scanners and reconstruction algorithms, as well as from local physiological variations between patients.
Once the patient's data is spatially normalized, intensity normalized and smoothed, it can be compared to the database, where, for example, the number of standard deviations each voxel data is away from the mean can be computed: voxels whose data is significantly different from the average may be an indication of disease.
The database itself is constructed by following the same process for each of the scans that will be included in it. Once all scans have been spatially normalized, intensity normalized and smoothed, statistics are computed for each voxel in the volume across all the scans (e.g., mean and standard deviation), giving rise to output data volumes containing these statistics, which populate the database.
This type of analysis is most often performed in brain scans, since the relative similarity of the anatomy of different subjects makes the process of spatial and intensity normalization simpler. However, the principle is not restricted to such an organ once the process of normalizations can be resolved.
Different scanners (both different models from the same manufacturer and models from different manufacturers) and different reconstruction protocols (both algorithm type and parameterisation) can result in significantly different degrees of smoothing in the resulting image (i.e., reduction in resolution) that is finally read by a clinician. When doing the kind of database comparison analysis described above, matching the degree of smoothing across the scans used to build the database, and also matching the degree of smoothing in any new patient scan with that used in the database construction is fundamental in trying to minimize diagnostically irrelevant differences that arise simply from differences in resolution between the particular scanner/reconstruction combination used for the scan. For example, in scans from a scanner/reconstruction combination that produces higher resolution images, when compared to those used to build the database, small regions of high uptake may be present that were not visible in the database images purely due to the lower resolution.
A previously considered method is to use a fairly large, constant, degree of smoothing, since adding additional smoothing is less detrimental to the overall process than not using enough. However, this assumption breaks down, for example, when the input data is too dissimilar to the database, and in this situation a new database must be generated using data that more closely matches the new input data. Even when this is done, the fact that a constant amount of smoothing is used means that each scan is not optimised for comparison to the database, meaning that differences may not show up as clearly as they otherwise might do.
The ability to reduce voxel-wise variance between brain PET images from different scanners has recently been reported by Joshi et al (2009) as part of a multi-centre observational study: the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this study, the physical Hoffman brain phantom was imaged on a range of different scanners, and the resulting images were further smoothed with a Gaussian filter to smooth all images to a common resolution. The additional smoothing factors were then applied to clinical data and demonstrated a reduction in differences between the scans.